This tutorial compares spectral resolution for several different sensors and the effect of resolution on the ability to discriminate and identify materials with distinct spectral signatures. The tutorial uses TM, GEOSCAN, GER63, and AVIRIS data from Cuprite, Nevada, USA, for intercomparison and comparison to materials from the USGS Spectral library.
You must have the ENVI TUTORIALS & DATA CD-ROM mounted on your system to access the files used by this tutorial, or copy the files to your disk.
Most of the files used in this tutorial are contained in the CUP_COMP subdirectory of the ENVIDATA directory on the ENVI TUTORIALS & DATA CD-ROM. The AVIRIS reflectance image file CUP95EFF.INT, its associated ENVI header file, and the extracted AVIRIS spectra are located in the C95AVSUB subdirectory.
The files listed below, along with their associated .hdr files, are required to run this exercise. Optional spectral library files listed below may also be used if more detailed comparisons are desired. Selected data files have been converted to integer format by multiplying the reflectance values by 1000 because of disk space considerations. Values of 1000 in the files represent reflectance values of 1.0.
USGS_EM.SLI Subset of USGS Spectral Library
USGS_EM.HDR ENVI Header for Above
CUPTM_RF.IMG Cuprite TM reflectance subset
CUPTM_RF.HDR ENVI Header for Above
CUPTM_EM.TXT Kaolinite and Alunite average spectra from above
CUPGS_SB.IMT Cuprite Geoscan Reflectance Image Subset
CUPGS_SB.HDR ENVI Header for above
CUPGS_TM.TXT Kaolinite and Alunite average spectra from above
CUPGERSB.IMG Cuprite GER64 Reflectance Image Subset
CUPGERSB.HDR ENVI Header for above
CUPGEREM.TXT Kaolinite and Alunite average spectra from above
CUP95EFF.INT Cuprite 1995 AVIRIS Reflectance Image Subset (in the C95AVSUB directory)
CUP95EFF.HDR ENVI Header from above (in the C95AVSUB directory)
CUP95EFF.TXT Kaolinite and Alunite average spectra from above in the C95AVSUB directory.
USGS_MIN.SLI USGS Spectral Library
USGS_MIN.HDR ENVI Header for Above
Spectral resolution determines the way we see individual spectral features in materials measured using imaging spectrometry. Many people confuse the terms spectral resolution with spectral sampling. These are very different. Spectral resolution refers to the width of an instrument response (band-pass) at half of the band depth (the Full Width Half Max [FWHM]). Spectral sampling usually refers to the band spacing - the quantization of the spectrum at discrete steps - and may be very different from the spectral resolution. Quality spectrometers are usually designed so that the band spacing is about equal to the band FWHM, which is why band spacing is often thought of as equal to spectral resolution. These are two different things, however, so be careful in your use of terms.
This exercise compares the effect of the spectral resolution of different sensors on the spectral signatures of minerals.
Spectral modeling shows that spectral resolution requirements for imaging spectrometers depend upon the character of the material being measured. For example, for the mineral kaolinite, shown in the plot below, we are still able to distinguish the characteristic doublet near 2.2 mm at 20 nm resolution. Even at 40 nm resolution, the asymmetrical shape of the band may be enough to identify the mineral, even though the spectral features have not been fully resolved.
Figure 1: Modeled effect of spectral resolution on the appearance of spectral features of kaolinite. Spectral resolution from top to bottom: 5, 10, 20, 40, and 80 nm resolution.
The spectral resolution required for a specific sensor is a direct function of the material you are trying to identify, and the contrast between that material and the background materials. The following figure shows modeled spectra for the mineral kaolinite for several different sensors.
Figure 2: Modeled signatures of different hyperspectral sensors for the mineral kaolinite. From Swayze, 1997.
This example is provided to illustrate the effects of spatial and spectral resolution on information extraction from multispectral/hyperspectral data. Several images of the Cuprite, Nevada, USA, area acquired with a variety of spectral and spatial resolutions serve as the basis for discussions on the effect of these parameters on mineralogic mapping using remote sensing techniques. These images have not been georeferenced, but image subsets covering approximately the same spatial areas are shown. Cuprite has been used extensively as a test site for remote sensing instrument validation (Abrams et al., 1978; Kahle and Goetz, 1983; Kruse et al., 1990; Hook et al., 1991). A generalized alteration map is provided for comparison with the images. Examples from Landsat TM, GEOSCAN MkII, GER63, and AVIRIS illustrate both spatial and spectral aspects.
Figure 3: Alteration map for Cuprite, Nevada.
All of these data sets have been calibrated to reflectance. Only three of the numerous materials present at the Cuprite site were used for the purposes of this comparison. Average kaolinite, alunite, and buddingtonite image spectra were selected from known occurrences at Cuprite. Laboratory spectra from the USGS Spectral Library (Clark et al., 1990) of the three selected minerals are provided for comparison to the image spectra. The following is a synopsis of selected instrument characteristics and a discussion of the images and spectra obtained with each sensor
Before attempting to start the program, ensure that ENVI is properly installed as described in the installation guide.
The ENVI Main Menu appears when the program has successfully loaded and executed.
To open a spectral library:
Note that on some platforms you must hold the left mouse button down to display the submenus from the Main Menu.
A file selection dialog appears.
The Spectral Library Viewer dialog appears with four laboratory spectra for the Cuprite site listed.
This step uses library Spectra (approximately 10 nm Spectral Resolution) from the USGS Spectral Library.
Figure 4: Laboratory measurements for the minerals kaolinite, alunite, and buddingtonite measured on the USGS Denver Beckman Spectrometer.
This dataset is Landsat Thematic Mapper data with spatial resolution of 30 meters and spectral resolution of up to 100nm. The Cuprite TM data were acquired on 4 October 1984 and are in the public domain. The figure below is a plot of the Region of Interest (ROI) average spectra for the three materials shown in the library spectra above. The small squares indicate the TM band 7 (2.21 mm) center point. The lines indicate the slope from TM band 5 (1.65 mm). Note the similarity of all of the "spectra" and how it is not possible to discriminate between the three endmembers.
This is the Landsat TM reflectance data for Cuprite, Nevada, produced using ENVI's Landsat TM calibration Utility.
This dataset is Cuprite GEOSCAN imagery with approximately 60 nm Resolution with 44 nm sampling converted to apparent reflectance using a flat field correction. The GEOSCAN MkII sensor, flown on a light aircraft during the late 1980s was a commercial aircraft system that acquired up to 24 spectral channels selected from 46 available bands. GEOSCAN covered the range from 0.45 to 12.0 mm using grating dispersive optics and three sets of linear array detectors (Lyon and Honey, 1989). A typical data acquisition for geology resulted in 10 bands in the visible/near infrared (VNIR, 0.52 - 0.96 mm), 8 bands in the shortwave infrared (SWIR, 2.04 - 2.35 mm), and thermal infrared (TIR, 8.64 - 11.28 mm) regions (Lyon and Honey, 1990). The GEOSCAN data were acquired in June 1989. The figure below is a plot of the ROI average spectra for the three materials shown in the library spectral plot above. Compare these to library spectra and the Landsat TM spectra and note that the three minerals appear quite different in the GEOSCAN data, even with the relatively widely spaced spectral bands.
GEOSCAN is high spatial resolution makes it suitable for detailed geologic mapping (Hook et al., 1991). The relatively low number of spectral bands and, low spectral resolution limit mineralogic mapping capabilities to a few groups of minerals in the absence of ground information. Strategic placement of the SWIR bands, however, does provide more mineralogic information than would intuitively be expected based on the spectral resolution limitations.
This dataset is Cuprite Geophysical and Environmental Research 63-band scanner data (GER63). It has an advertised spectral resolution of 17.5 nm, but comparison with other sensors and laboratory spectra suggests that 35 nm resolution with 17.5 nm sampling is more likely. Four bad bands have been dropped so that only 59 spectral bands are available. The GER63 data described here were acquired during August 1987. Selected analysis results were previously published in Kruse et al. (1990). The figure below is a plot of the ROI average spectra for the three materials shown in the library spectra above. Note that the GER63 data adequately discriminate the alunite and buddingtonite, but do not fully resolve the kaolinite "doublet" near 2.2 mm shown in the laboratory.
These data are Cuprite 1995 Airborne Visible Infrared Imaging Spectrometer (AVIRIS), which have approximately 10 nm spectral resolution and 20 m spatial resolution. The AVIRIS data shown here were acquired during July 1995 as part of an AVIRIS Group Shoot (Kruse and Huntington, 1996). The data were corrected to reflectance using the ATREM method and residual noise was removed using the EFFORT procedure. The figure below is a plot of the ROI average spectra for the three materials shown in the library spectral plot. Compare these to the laboratory spectra above and note the high quality and nearly identical signatures.
These four sensors and the library spectra represent a broad range of spectral resolutions.
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